






'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Union[str, Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Union[str, Any]  =					{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"trocr"
					a_        =					["past_key_values"]
					a_        =					{
					    "num_attention_heads": "decoder_attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "decoder_layers",
					}




					def __init__(  self  :		Any				, _A  :		Union[str, Any]=5_02_65				, _A  :		int=10_24				, _A  :		int=12				, _A  :		str=16				, _A  :		Optional[int]=40_96				, _A  :		Optional[Any]="gelu"				, _A  :		int=5_12				, _A  :		Optional[int]=0.1				, _A  :		Dict=0.0				, _A  :		str=0.0				, _A  :		Union[str, Any]=2				, _A  :		str=0.02				, _A  :		Union[str, Any]=0.0				, _A  :		List[Any]=True				, _A  :		Any=False				, _A  :		Any=True				, _A  :		List[Any]=True				, _A  :		Any=1				, _A  :		Any=0				, _A  :		Dict=2				, **_A  :		List[str]				, )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Dict          =       vocab_size
								UpperCAmelCase_      :       Optional[Any]          =       d_model
								UpperCAmelCase_      :       Dict          =       decoder_layers
								UpperCAmelCase_      :       Any          =       decoder_attention_heads
								UpperCAmelCase_      :       List[str]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[str]          =       activation_function
								UpperCAmelCase_      :       Any          =       max_position_embeddings
								UpperCAmelCase_      :       Any          =       dropout
								UpperCAmelCase_      :       int          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Optional[int]          =       init_std
								UpperCAmelCase_      :       Optional[Any]          =       decoder_layerdrop
								UpperCAmelCase_      :       Optional[Any]          =       use_cache
								UpperCAmelCase_      :       Any          =       scale_embedding
								UpperCAmelCase_      :       Dict          =       use_learned_position_embeddings
								UpperCAmelCase_      :       List[str]          =       layernorm_embedding

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, decoder_start_token_id=_A				, **_A				, )










'''simple docstring'''





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
						from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor


class  snake_case__      :




					def __init__(  self  :		Union[str, Any]				, _A  :		Optional[Any]				, _A  :		Dict=2				, _A  :		Optional[Any]=32				, _A  :		List[str]=16				, _A  :		str=3				, _A  :		List[Any]=True				, _A  :		Optional[int]=True				, _A  :		Optional[int]=32				, _A  :		Optional[Any]=4				, _A  :		Optional[int]=[0, 1, 2, 3]				, _A  :		List[Any]=4				, _A  :		Optional[int]=37				, _A  :		Optional[Any]="gelu"				, _A  :		Optional[Any]=0.1				, _A  :		Union[str, Any]=0.1				, _A  :		Dict=0.02				, _A  :		Optional[Any]=3				, _A  :		Union[str, Any]=[1, 3_84, 24, 24]				, _A  :		int=True				, _A  :		int=None				, )    ->							Tuple:
								UpperCAmelCase_      :       Tuple          =       parent
								UpperCAmelCase_      :       List[str]          =       batch_size
								UpperCAmelCase_      :       Union[str, Any]          =       image_size
								UpperCAmelCase_      :       List[Any]          =       patch_size
								UpperCAmelCase_      :       Dict          =       num_channels
								UpperCAmelCase_      :       str          =       is_training
								UpperCAmelCase_      :       List[str]          =       use_labels
								UpperCAmelCase_      :       Optional[Any]          =       hidden_size
								UpperCAmelCase_      :       str          =       num_hidden_layers
								UpperCAmelCase_      :       int          =       backbone_out_indices
								UpperCAmelCase_      :       str          =       num_attention_heads
								UpperCAmelCase_      :       List[Any]          =       intermediate_size
								UpperCAmelCase_      :       Union[str, Any]          =       hidden_act
								UpperCAmelCase_      :       Dict          =       hidden_dropout_prob
								UpperCAmelCase_      :       Union[str, Any]          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       List[str]          =       initializer_range
								UpperCAmelCase_      :       int          =       num_labels
								UpperCAmelCase_      :       Union[str, Any]          =       backbone_featmap_shape
								UpperCAmelCase_      :       List[str]          =       scope
								UpperCAmelCase_      :       List[str]          =       is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_      :       List[Any]          =       (image_size // patch_size) ** 2
								UpperCAmelCase_      :       List[str]          =       num_patches + 1




					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[int]          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								UpperCAmelCase_      :       List[str]          =       None
								if self.use_labels:
											UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.image_size, self.image_size]				, self.num_labels      )

								UpperCAmelCase_      :       Dict          =       self.get_config()

								return config, pixel_values, labels




					def 		A      (  self  :		int      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       {
								    '''global_padding''': '''same''',
								    '''layer_type''': '''bottleneck''',
								    '''depths''': [3, 4, 9],
								    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
								    '''embedding_dynamic_padding''': True,
								    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
								    '''num_groups''': 2,
								}

								return DPTConfig(
								    image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, backbone_out_indices=self.backbone_out_indices				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, is_decoder=_A				, initializer_range=self.initializer_range				, is_hybrid=self.is_hybrid				, backbone_config=_A				, backbone_featmap_shape=self.backbone_featmap_shape				, )




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[Any]				, _A  :		Optional[int]				, _A  :		Optional[int]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Optional[int]          =       DPTModel(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       List[str]          =       model(_A      )
								self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)      )




					def 		A      (  self  :		Tuple				, _A  :		str				, _A  :		Tuple				, _A  :		Union[str, Any]      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.num_labels
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       int          =       model(_A      )
								self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)      )




					def 		A      (  self  :		List[str]				, _A  :		str				, _A  :		Union[str, Any]				, _A  :		int      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.num_labels
								UpperCAmelCase_      :       Optional[Any]          =       DPTForSemanticSegmentation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       Tuple          =       model(_A				, labels=_A      )
								self.parent.assertEqual(
								    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)      )





					def 		A      (  self  :		Dict      )    ->							Any:
								UpperCAmelCase_      :       Optional[int]          =       self.prepare_config_and_inputs()
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       config_and_inputs
								UpperCAmelCase_      :       Tuple          =       {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					a_        =					(
					    {
					        "depth-estimation": DPTForDepthEstimation,
					        "feature-extraction": DPTModel,
					        "image-segmentation": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					a_        =					False
					a_        =					False
					a_        =					False




					def 		A      (  self  :		Tuple      )    ->							Tuple:
								UpperCAmelCase_      :       Any          =       DPTModelTester(self      )
								UpperCAmelCase_      :       Dict          =       ConfigTester(self				, config_class=_A				, has_text_modality=_A				, hidden_size=37      )




					def 		A      (  self  :		Optional[int]      )    ->							int:
								self.config_tester.run_common_tests()




					@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								pass




					def 		A      (  self  :		str      )    ->							Dict:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Union[str, Any]          =       model_class(_A      )
											self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)      )
											UpperCAmelCase_      :       Dict          =       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_A				, nn.Linear      )      )




					def 		A      (  self  :		List[Any]      )    ->							Tuple:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Dict          =       model_class(_A      )
											UpperCAmelCase_      :       Optional[Any]          =       inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCAmelCase_      :       int          =       [*signature.parameters.keys()]

											UpperCAmelCase_      :       Any          =       ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				, _A      )




					def 		A      (  self  :		List[str]      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					def 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_A      )




					def 		A      (  self  :		str      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_A      )




					def 		A      (  self  :		Tuple      )    ->							str:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       Union[str, Any]          =       True

											if model_class in get_values(_A      ):
														continue

											UpperCAmelCase_      :       Optional[int]          =       model_class(_A      )
											model.to(_A      )
											model.train()
											UpperCAmelCase_      :       Any          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       Tuple          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Union[str, Any]      )    ->							Tuple:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       List[Any]          =       False
											UpperCAmelCase_      :       Optional[Any]          =       True

											if model_class in get_values(_A      ) or not model_class.supports_gradient_checkpointing:
														continue
											UpperCAmelCase_      :       int          =       model_class(_A      )
											model.to(_A      )
											model.gradient_checkpointing_enable()
											model.train()
											UpperCAmelCase_      :       int          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       List[str]          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase_      :       List[str]          =       _config_zero_init(_A      )
								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       int          =       model_class(config=_A      )
											# Skip the check for the backbone
											UpperCAmelCase_      :       List[str]          =       []
											for name, module in model.named_modules():
														if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																	UpperCAmelCase_      :       Union[str, Any]          =       [F"{name}.{key}" for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1e9).round() / 1e9).item()				, [0.0, 1.0]				, msg=F"Parameter {name} of model {model_class} seems not properly initialized"				, )




					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
					def 		A      (  self  :		Tuple      )    ->							Optional[Any]:
								pass




					@slow
					def 		A      (  self  :		Any      )    ->							int:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											UpperCAmelCase_      :       Any          =       DPTModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )





					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_      :       Optional[int]          =       '''add'''
								with self.assertRaises(_A      ):
											UpperCAmelCase_      :       Optional[Any]          =       DPTForDepthEstimation(_A      )






def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       List[Any]          =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image







@require_torch
@require_vision
@slow
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       List[Any]          =       DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(_A      )

								UpperCAmelCase_      :       Tuple          =       prepare_img()
								UpperCAmelCase_      :       Union[str, Any]          =       image_processor(images=_A				, return_tensors='''pt'''      ).to(_A      )

								# forward pass
								with torch.no_grad():
											UpperCAmelCase_      :       str          =       model(**_A      )
											UpperCAmelCase_      :       List[Any]          =       outputs.predicted_depth

								# verify the predicted depth
								UpperCAmelCase_      :       str          =       torch.Size((1, 3_84, 3_84)      )
								self.assertEqual(predicted_depth.shape				, _A      )

								UpperCAmelCase_      :       Tuple          =       torch.tensor(
								    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]      ).to(_A      )

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00				, _A				, atol=1e-4      )      )


